{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4be427e3",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "#  Mplfinance Used To Plot Alfhatrend Indicator\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db022190",
   "metadata": {},
   "source": [
    "### What is Alphatrend Indicator\n",
    "The Alpha Trend indicator is a powerful tool that traders use to identify trends and trading points in the market. It addresses four key elements:\n",
    "\n",
    "- Reducing false signals at sideway market by trading less frequently\n",
    "- Creating a meaningful trading system by combining indicators from different categories\n",
    "- Establishing reliable buy and sell points\n",
    "- Identifying potential support and resistance levels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abfc3353",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "### mplfinance 'yahoo' styles was used to customize alphatrend indicator:\n",
    "- Type of Plot Use `candle`\n",
    "- Alphatrend Indicator Build With Two Types Lines Named Signal Line and Trend Line\n",
    "- Background, Grid, and Figure Colors\n",
    "- Grid style\n",
    "- Y-Axis On The Right or Left\n",
    "- Matplotlib Defaults\n",
    "- Fill Between\n",
    "- Alpha\n",
    "- Color\n",
    "#### The simplest way to do this is to choose one of the `add_plot` that come packaged with `mplfinance`\n",
    "#### but, as we see below, it is also possible to customize your own `mplfinance styles`.\n",
    "#### Also Other Plot Type Can Be Used\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "821ec5c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pandas_ta as ta\n",
    "import mplfinance as mpf\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "378febfd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This allows multiple outputs from a single jupyter notebook cell:\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b32a433d",
   "metadata": {},
   "source": [
    "### Read in daily data for the S&P 500 from November of 2019 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a410f0a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 6)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2003-06-25</th>\n",
       "      <td>20.530001</td>\n",
       "      <td>20.83</td>\n",
       "      <td>19.99</td>\n",
       "      <td>20.040001</td>\n",
       "      <td>13.693501</td>\n",
       "      <td>61250600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-06-26</th>\n",
       "      <td>20.299999</td>\n",
       "      <td>20.76</td>\n",
       "      <td>20.15</td>\n",
       "      <td>20.629999</td>\n",
       "      <td>14.096654</td>\n",
       "      <td>52904900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Open   High    Low      Close  Adj Close    Volume\n",
       "Date                                                               \n",
       "2003-06-25  20.530001  20.83  19.99  20.040001  13.693501  61250600\n",
       "2003-06-26  20.299999  20.76  20.15  20.629999  14.096654  52904900"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2004-04-07</th>\n",
       "      <td>28.08</td>\n",
       "      <td>28.129999</td>\n",
       "      <td>27.480000</td>\n",
       "      <td>27.620001</td>\n",
       "      <td>18.923342</td>\n",
       "      <td>72680200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-04-08</th>\n",
       "      <td>28.08</td>\n",
       "      <td>28.139999</td>\n",
       "      <td>27.200001</td>\n",
       "      <td>27.370001</td>\n",
       "      <td>18.752058</td>\n",
       "      <td>71791400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open       High        Low      Close  Adj Close    Volume\n",
       "Date                                                                   \n",
       "2004-04-07  28.08  28.129999  27.480000  27.620001  18.923342  72680200\n",
       "2004-04-08  28.08  28.139999  27.200001  27.370001  18.752058  71791400"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idf = pd.read_csv('../data/yahoofinance-INTC-19950101-20040412.csv',index_col=0,parse_dates=True).tail(200)\n",
    "\n",
    "df = idf.copy()\n",
    "df.index.name = 'Date'\n",
    "df.shape\n",
    "df.head(2)\n",
    "df.tail(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9157386c",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "- `alphatrend_cal` is OHLCV Data To Return Trend And Signal Line Used In Alphatrend\n",
    "- **Here is Function For The Calculation:**\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5c3e9613",
   "metadata": {},
   "outputs": [],
   "source": [
    "def alphatrend_cal(df):  \n",
    "    Open = df['Open']\n",
    "    Close = df['Close']\n",
    "    High = df['High']\n",
    "    Low = df['Low']\n",
    "    Volume = df['Volume']\n",
    "    ap = 14\n",
    "    tr = ta.true_range(High, Low, Close)\n",
    "    atr = ta.sma(tr, ap)\n",
    "    noVolumeData = False\n",
    "    coeff = 1\n",
    "    upt = []\n",
    "    downT = []\n",
    "    AlphaTrend = [0.0]\n",
    "    src = Close\n",
    "    rsi = ta.rsi(src, 14)\n",
    "    hlc3 = []\n",
    "    k1 = []\n",
    "    k2 = []\n",
    "    mfi = ta.mfi(High, Low, Close, Volume, 14)\n",
    "    for i in range(len(Close)):\n",
    "        hlc3.append((High[i] + Low[i] + Close[i]) / 3)\n",
    "\n",
    "    for i in range(len(Low)):\n",
    "        if pd.isna(atr[i]):\n",
    "            upt.append(0)\n",
    "        else:\n",
    "            upt.append(Low[i] - (atr[i] * coeff))\n",
    "    for i in range(len(High)):\n",
    "        if pd.isna(atr[i]):\n",
    "            downT.append(0)\n",
    "        else:\n",
    "            downT.append(High[i] + (atr[i] * coeff))\n",
    "    for i in range(1, len(Close)):\n",
    "        if noVolumeData is True and rsi[i] >= 50:\n",
    "            if upt[i] < AlphaTrend[i - 1]:\n",
    "                AlphaTrend.append(AlphaTrend[i - 1])\n",
    "            else:\n",
    "                AlphaTrend.append(upt[i])\n",
    "\n",
    "        elif noVolumeData is False and mfi[i] >= 50:\n",
    "            if upt[i] < AlphaTrend[i - 1]:\n",
    "                AlphaTrend.append(AlphaTrend[i - 1])\n",
    "            else:\n",
    "                AlphaTrend.append(upt[i])\n",
    "        else:\n",
    "            if downT[i] > AlphaTrend[i - 1]:\n",
    "                AlphaTrend.append(AlphaTrend[i - 1])\n",
    "            else:\n",
    "                AlphaTrend.append(downT[i])\n",
    "\n",
    "    for i in range(len(AlphaTrend)):\n",
    "        if i < 2:\n",
    "            k2.append(0)\n",
    "            k1.append(AlphaTrend[i])\n",
    "        else:\n",
    "            k2.append(AlphaTrend[i - 2])\n",
    "            k1.append(AlphaTrend[i])\n",
    "\n",
    "    df['k1'] = k1\n",
    "    df['k2'] = k2\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e84ec985",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "- `alphatrend_cal` return new DataFrame With Two New Columns Named As `k1` and `k2`\n",
    "- **Here is New Dataframe Named As alphatrend:**\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6b097e76",
   "metadata": {},
   "outputs": [],
   "source": [
    "alphatrend = alphatrend_cal(df).tail(90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "84416a60",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Extracted And New Variable Applied\n",
    "k1 = alphatrend[['k1']]\n",
    "k2 = alphatrend[['k2']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc1e77f1",
   "metadata": {},
   "source": [
    "Use a dict to specify other attributes (kwargs) for `fill_between`:\n",
    "<br>\n",
    "To demonstrate use of `fill_between` the `where` kwarg to display a holding period\n",
    "<br>\n",
    "`where = aplhatrend['k1'] < alphatrend['k2']`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2a1415c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "fill_up = dict(y1 = alphatrend['k1'].values, y2 = alphatrend['k2'].values, where = alphatrend['k1'] >= alphatrend['k2'], color = '#00E60F')\n",
    "fill_down = dict(y1 = alphatrend['k1'].values, y2 = alphatrend['k2'].values, where = alphatrend['k1'] <= alphatrend['k2'], color = '#80000B')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4053fab9",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "Let's say we want to plot the Ichimoku Cloud along with the basic OHLCV plot.  \n",
    "\n",
    "We Use `make_addplot()` to create the addplot dict, and pass that into the plot() function:\n",
    "\n",
    "We Use `Color` To Define Line Colors\n",
    "\n",
    "We Use `alpha` To Define Depth Line Color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3cc8fd1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "ic = [\n",
    "    #Alpha Trend\n",
    "    mpf.make_addplot(k1,color = '#0022FC',width=3),\n",
    "    mpf.make_addplot(k2,color = '#FC0400',width=3),\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "236c94af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpf.plot(\n",
    "    df.tail(90),\n",
    "    #volume=True,\n",
    "    type=\"candle\", \n",
    "    style=\"yahoo\",\n",
    "    addplot=ic,\n",
    "    fill_between = [fill_up,fill_down],\n",
    "    figsize=(20,10)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ac1cbf1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
